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Update KMeans init docstring to include 'random' option #1020

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Update KMeans init docstring to include 'random' option #1020

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11 changes: 8 additions & 3 deletions dask_ml/cluster/k_means.py
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Expand Up @@ -36,19 +36,24 @@ class KMeans(TransformerMixin, BaseEstimator):
----------
n_clusters : int, default 8
Number of clusters to end up with
init : {'k-means||', 'k-means++' or ndarray}
init : {'k-means||', 'k-means++', 'random' or ndarray}
Method for center initialization, defaults to 'k-means||'.

'k-means||' : selects the the gg
'k-means||' : Selects initial cluster centers using a scalable
variant of k-means++. See the notes for more details.

'k-means++' : selects the initial cluster centers in a smart way
'k-means++' : Selects the initial cluster centers in a smart way
to speed up convergence. Uses scikit-learn's implementation.

.. warning::

If using ``'k-means++'``, the entire dataset will be read into
memory at once.

'random' : Selects `n_clusters` random rows from the input data for
the initial centroids. Use `n_init` to run multiple random
initializations for more robust results.

An array of shape (n_clusters, n_features) can be used to give
an explicit starting point

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